Communications engineering最新文献

Perovskite solar cells (PSCs) hold promise for high-efficiency photovoltaic technology but face commercialization challenges due to scaling difficulties. A common approach for scaling PSCs involves creating perovskite solar modules (PSMs) with subcells connected in series, using P1, P2, and P3 laser scribing process to reduce interconnection losses. In this study, a standard nanosecond pulse UV laser was used to perform these scribes. Here we demonstrated that, by employing a single 45 µm laser line for each scribe, it can significantly reduce the dead area, resulting in exceptionally high geometric fill factors (GFFs). In inverted PSMs with active areas of 4.0 cm² and 10.8 cm², it was reached GFFs of 99.3% and 98.8%, respectively. To the best of author's knowledge, this work demonstrates the first successful use of a single nanosecond laser source for continuous P1-P2-P3 scribing, achieving a dead area as low as 0.7% in a 4 cm² module.

The excitation and inhibition (E/I) balance of neural circuits is a crucial index of neurophysiological homeostasis associated with healthy brain functioning. Although several cutting-edge methods exist to assess E/I balance in an intact brain, they have inherent limitations, such as difficulties in tracking changes in E/I balance over time. To address this, we introduced neural-mass-model-based tracking using a data assimilation (DA) approach. While we previously demonstrated that sleep-dependent E/I changes could be estimated from electroencephalography (EEG) data, the neurophysiological validity of this method had not been directly evaluated. In this study, we developed an enhanced DA-based method and compared its E/I estimates with the concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) based measures. Our results revealed significant correlations between the DA-based estimates and TMS-EEG indices of E/I balance in the dorsolateral prefrontal cortex. These findings indicate that our computational approach provides neurophysiologically valid estimations of time-varying E/I balance.

We report on the design and fabrication of a circular pillar array as an interfacial barrier for microfluidic microphysiological systems (MPS). Traditional barrier interfaces, such as porous membranes and microchannel arrays, present limitations due to inconsistent pore size, complex fabrication and device assembly, and a lack of tunability using a scalable design. Our pillar array overcomes these limitations by providing precise control over pore size, porosity, and hydraulic resistance through simple modifications of pillar dimensions. Serving as an interface between microfluidic compartments, it facilitates cell aggregation for tissue formation and acts as a tunable diffusion barrier that mimics diffusion in vivo. We demonstrate the utility of barrier design to engineer physiologically relevant cardiac microtissues and a heterotypic model with vasculature within the device. The tunable properties offer significant potential for drug screening/testing and disease modeling, enabling comparisons of drug permeability and cell migration in MPS tissue with or without vasculature.

Current in silico dialyser models remain accurate only under narrowly constrained conditions. Advances in computing power and increasing levels of interdisciplinary collaboration, however, now set the stage for more robust computational haemodialyser models. In this review, we survey existing modelling approaches-long used to guide haemodialyser design and clinical nephrology practice-identify key unresolved challenges, and propose computational strategies poised to accelerate the development of dialyser technologies that better meet the needs of kidney failure patients.

Accurate estimation of core body temperature (CBT) is essential for physiological monitoring, yet current non-invasive methods lack statistically calibrated uncertainty estimates required for safety-critical use. Here we introduce a conformal deep learning framework for real-time, non-invasive CBT prediction with calibrated uncertainty, demonstrated in high-risk heat-stress environments. Developed from over 140,000 physiological measurements across six operational domains, the model achieves a test error of 0.29 °C, outperforming the widely used ECTemp™ algorithm with a 12-fold improvement in calibrated probabilistic accuracy and statistically valid prediction intervals. Designed for integration with wearable devices, the system uses accessible physiological, demographic, and environmental inputs to support practical, confidence-informed monitoring. A customizable alert engine enables proactive safety interventions based on user-defined thresholds and model confidence. By combining deep learning with conformal prediction, this approach establishes a generalizable foundation for trustworthy, non-invasive physiological monitoring, demonstrated here for CBT under heat stress but applicable to broader safety-critical settings.

Electrostatic actuators typically rely on Maxwell stress, whereas the use of transverse electrostatic force (TEF) has been overlooked because of its weakness. The discovery of polar nematic liquid crystals in 2017 changed this, enabling lower operating voltages and high output power. We here explore TEF in a ferroelectric fluid and demonstrate a ferroelectric motor based on a new driving principle. Using ferroelectric nematic liquid crystals, we show that TEF can elevate the fluid between electrodes with a gap of 2.5 mm up to more than 80 mm at only 28 V mm^-1, corresponding to a stress greater than 1000 N m^-2. Polarization analysis also revealed a continuous paraelectric-to-ferroelectric transition. Unlike electromagnetic motors, ferroelectric motors require no metal rotors or magnets, resulting in a lower weight and simplified device structure and eliminating the need for rare-earth materials. These results suggest that ferroelectric fluids can enhance electrostatic actuator performance and practicability.

Uncrewed underwater vehicles(UUVs) play an indispensable role in ocean resource efficiency utilization due to their security and cost-effectiveness. However, the significant uncertainties, time-varying disturbances, and unstructured subsea environments pose challenges for UUVs in achieving precise and efficient marine missions. To address these challenges, this study introduces a novel cooperative control framework for UUVs. The proposed framework minimizes the high control efforts of sliding mode control while preserving robustness, enabling efficiency and precision for long-duration dynamic hovering missions of UUVs. Specifically, a key innovation is the development of a deviation separation strategy, which, for the first time, decouples hovering deviations into task-specific and anti-disturbance components using an influence function. This enables real-time disturbance estimation without prior knowledge enabling adaptive disturbance compensation. By cooperating between LQR and SMC, the proposed method avoids the performance conflicts commonly observed in single-controller schemes. This structure improves compensation accuracy, robustness to disturbances, and energy efficiency across various operating conditions. The results demonstrate that the proposed cooperative control strategy effectively counters current perturbations by leveraging the real-time insights from the error segregation, while concurrently executing high-precision hovering tasks with low control costs. This work advances UUVs control, offering a versatile solution for complex underwater tasks.

The rise of the Internet of Things (IoT) is creating new communication needs that require us to completely rethink how we design wireless interaction between potentially diverse devices, which must be increasingly environmentally friendly and agile. This article introduces a wireless communication method elegantly grounded in the principle of the RF feedback loop. Unlike traditional communication methods, this approach does not require the reader to emit any waves in the absence of a transponder. Instead, communication occurs when a self-sustaining wave spontaneously emerges as the transponder enters the reader's reading zone, forming an active feedback loop between the two devices. This novel method of transmitting RF signals creates a new paradigm in the communications field that will significantly influence modes of interaction, especially in the IoT. This method significantly decreases the emission of unnecessary RF waves and ensures complete discretion for the reader when no transponder is within range. The principle of Wireless Active Feedback Loop Communication (WAFLC) has been validated through both simulations and practical tests, showing a strong correlation between the two.

Cascaded guidance, state estimation, and control systems have been successfully implemented in autonomous underwater vehicles. However, disordered convergence sequences among subsystems fundamentally induce system oscillations and instabilities. Here, we introduce a temporal-sequencing-convergent cascaded guidance, state estimation, and control system for depth-tracking of underactuated autonomous underwater vehicles. It establishes an ordered convergence architecture: state estimation converges first with a planning window t_d, followed by control execution with a planning time window t_c, and guidance finally converges with a planning time window t_f, adhering to a temporal-sequencing-convergent criterion t_d < t_c < t_f. This architecture, validated through experiments, ensures stable and efficient depth-tracking performance owing to well-ordered convergence of subsystems. Conversely, experiments violating the temporal-sequencing-convergent criterion exhibited prominent oscillatory depth-tracking responses. Our method outperformed selected approaches, achieving a lower average depth-tracking error of 1.32 cm under sudden external disturbances. Notably, even with prominent pitch-tracking errors using a PD controller, our proposed guidance design also showcased remarkable attack-angle compensation capability, thereby maintaining precise depth-tracking performance.

MXenes offer a unique platform for designing high-performance electronic devices due to their diverse properties and chemical tunability. This study focuses on engineering low-resistance metal-semiconductor contacts using MXenes for future field-effect transistor applications. Through a comprehensive approach combining first-principles calculations, transport simulations, and alloy phase engineering, we demonstrate the feasibility of achieving low-resistance contacts with high current-carrying capacity. Through first-principles calculations, we identify promising MXene heterojunctions based on lattice matching and Schottky barrier height. Notably, the Ta₂CO₂-Ti₂CO₂ contact exhibits a remarkably low Schottky barrier height. Using non-equilibrium Green's function calculations, we demonstrate high output current in this contact, indicating low resistance. Further analysis reveals the critical role of carrier density and detrimental impact of metal-induced gap states. To suppress metal-induced gap states, we propose an interfacial alloying strategy using a Ta_2xTi_2(1-x)CO₂ solid solution, which reduces interfacial charge transfer and promotes smoother electronic coupling. This, in turn, reduces the Fermi-level pinning effect and contributes to a substantial reduction in contact resistance across the MXene interface. This study highlights the potential of MXenes as building blocks for advanced electronics and provides a pathway for engineering high-performance contacts through a combined computational and design approach.